AIMC Topic: Ontario

Clear Filters Showing 1 to 10 of 52 articles

High-Throughput Computing to Detect Harmful Drug-Drug Interactions in Older Adults: Protocol for a Population-Based Cohort Study.

JMIR research protocols
BACKGROUND: Drug-drug interactions (DDIs) are a major concern, especially for older adults taking multiple medications. Although Health Canada and the US Food and Drug Administration (FDA) use population-based studies to identify adverse drug events,...

Testing regular expression searches and machine learning models to determine housing instability and low income status from primary care electronic medical record data in Toronto, Ontario.

BMC public health
BACKGROUND: Housing and income are important social determinants of health (SDoH). Primary care providers often do not have information about these determinants, which could be used to support equitable health system planning and care delivery. The a...

Testing the Acceptability and Feasibility of a Gender-Informed Smoking Cessation mHealth App for Women: Mixed Methods Approach.

JMIR human factors
BACKGROUND: Cigarette smoking is a leading cause of preventable morbidity and mortality worldwide. Women who smoke face greater health risks than men, including higher rates of cardiovascular disease and more pronounced declines in lung function. Des...

Refining Air Pollution Exposure Estimates: A Comparison of Citywide and Neighborhood Land Use Regression Models in Toronto.

Environmental science & technology
Land use regression (LUR) models assess air pollution exposure but often struggle with transferability (predicting concentrations in areas without measurements) and generalizability (capturing spatial patterns across neighborhoods). This study evalua...

Detecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI Models.

JAMA network open
IMPORTANCE: Clinical artificial intelligence (AI) systems are susceptible to performance degradation due to data shifts, which can lead to erroneous predictions and potential patient harm. Proactively detecting and mitigating these shifts is crucial ...

Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data.

Scientific reports
Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder (ASD). ASD is typically detected using a case-finding approach based on symptoms and family history,...

Predicting Clinical Outcomes at the Toronto General Hospital Transitional Pain Service via the Manage My Pain App: Machine Learning Approach.

JMIR medical informatics
BACKGROUND: Chronic pain is a complex condition that affects more than a quarter of people worldwide. The development and progression of chronic pain are unique to each individual due to the contribution of interacting biological, psychological, and ...

Machine learning prediction of premature death from multimorbidity among people with inflammatory bowel disease: a population-based retrospective cohort study.

CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne
BACKGROUND: Multimorbidity, the co-occurrence of 2 or more chronic conditions, is important in patients with inflammatory bowel disease (IBD) given its association with complex care plans, poor health outcomes, and excess mortality. Our objectives we...

Of Lyme disease and machine learning in a One Health world.

American journal of veterinary research
OBJECTIVE: Lyme disease is a vector-borne emerging zoonosis in Ontario driven by human population growth and climate change. Lyme disease is also a prime example of the One Health concept. While little can be done to immediately reverse climate chang...

A Cross-Sectional Survey of Optometrists in Canada Regarding Referral Patterns and a Needs Assessment for an Artificial Intelligence Referral Screening Tool for Epiretinal Membrane.

Ophthalmic surgery, lasers & imaging retina
BACKGROUND AND OBJECTIVE: This study evaluated optometrists' referral patterns for epiretinal membrane (ERM) patients in Ontario, Canada, and their attitudes towards an artificial intelligence (AI) tool for improving referral accuracy. An anonymous o...